Methods and tools for diagnosing insulin resistance and assessing health status using nmr relaxation times for water

ABSTRACT

The subject invention pertains to a method that involves at least three steps: (1) acquisition of a NMR data set or spin relaxation curve for plasma, serum or whole blood samples, or for tissues monitored from outside the body, (2) analysis of the NMR data or relaxation curve to extract the T 2  and/or T 1  relaxation times for water (or surrogates thereof), and (3) conversion of the water T 2  and/or T 1  values (or surrogates thereof) into a measure of someone&#39;s health status (referred to as a T 2  or T 1  health score depending on the value (T 1  or T 2  or both T 1  and T 2 ) associated with the score). The T 1  and/or T 2  health score utilizes a statistical database derived from previous studies of subjects ranging from normal, healthy individuals to those having varying degrees of hidden or non-hidden metabolic abnormalities, such as inflammation, insulin resistance, lipid abnormalities (dyslipidemia), oxidative stress, brain abnormalities, cognitive impairment or other disorders, and provides a measure of a subject&#39;s overall metabolic and brain health status.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 62/113,112, filed Feb. 6, 2015, and 62/155,852, filed May 1, 2015,the disclosures of which are hereby incorporated by reference in theirentirety, including all figures, tables and amino acid or nucleic acidsequences.

BACKGROUND OF THE INVENTION

Conventional proteomics uses mass spectrometry to measure a large numberof protein biomarkers to establish profiles of health and disease (1).The subject application monitors changes in blood protein profiles bymeasuring just one biomarker: water T₂. Conventional proteomics focuseson the less abundant proteins in blood or body fluids after removing themost abundant ones during pre-treatment prior to analysis (2, 3). Incontrast, the subject application has developed a technique, termed“inverse proteomics” that involves no pre-treatment or samplemanipulation and leverages the information content of all blood proteinsand lipoproteins, including the most abundant ones.

BRIEF SUMMARY OF THE INVENTION

This application provides a means for developing an inexpensive bloodtest for front-line health screening and monitoring. Also, this test canbe used for the diagnosis of insulin resistance syndrome, an earlymetabolic abnormality that leads to type 2 diabetes. The test analyzesthe spin relaxation times (T₂ and/or T₁ or surrogates of T₂ and/or T₁)of water in plasma, serum or whole blood using nuclear magneticresonance (NMR). The blood samples can be obtained using a conventionalneedle stick or finger prick. However, given the intensity of the waterNMR signal, it should be feasible to monitor the relaxation times ofwater in blood from outside of the body using a NMR-enabled fingerprobe, earlobe clip or a wristwatch-like device linked to a smart phone.Portable NMR devices are already available (1). The NMR T₂ (orsurrogates thereof) for water reports on the concentration and chemicalstate of the proteins and lipoproteins in the blood. We refer to thisapproach as inverse proteomics.

The subject application has determined that lower water T₂ and/or T₁values (or surrogate values for T₂ and/or T₁) in serum and plasma areindicative of increasing degrees of metabolic dysfunction, even in anessentially healthy population with clinical lab values that fall in thenormal reference ranges. The unique value of time-domain nuclearmagnetic resonance (TD-NMR) is that an individual's overall healthstatus with respect to insulin resistance, inflammation, dyslipidemiaand acid-base abnormalities can be assessed simultaneously in onemeasurement without having to survey a large panel of clinical lab testsor biomarkers, which is expensive and impractical. Given its simplicity,water T₂ and/or T₁ (4), or surrogates of T₂ and/or T₁, can serve as ascreening tool for the early identification of individuals with hiddenrisk for diseases that are linked with metabolic abnormalities.Non-limiting examples of such diseases include, but are not limited to,diabetes, coronary artery disease, and Alzheimer's disease (5, 6). Thesedisorders account for much of the morbidity and mortality in modernsocieties. There is a continuing need for effective screening tools thatcan be implemented practically, inexpensively and broadly across thepopulation. Such tools will have a place in P4 medicine: personal,predictive, preventative and participatory medicine (7). The inventiondisclosed herein provides a solution to this continuing need.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 displays scatterplots for the bivariate correlations betweenplasma water T₂ values and diagnostic markers for the human subjectsenrolled in this study. Each black circle represents a data point for anindividual human subject enrolled in this study. Values are enclosed ina density ellipse calculated from the bivariate normal distribution fitto the X and Y variables at the 95% confidence level. It provides agraphical indication of the correlation between the variables. upperleft: plasma water T₂ vs. ln insulin C-peptide; upper right: plasmawater T₂ vs. the McAuley Index for insulin sensitivity; middle left:plasma water T₂ vs. total serum protein concentration; middle right:plasma water T₂ vs. LDL particle number; lower left: serum water T₂ vs.hs-CRP; lower right: serum water T₂ vs. ln white blood cell count.

FIG. 2 plots the correlation between plasma water T₂ and the number ofearly insulin resistance measures that were positive in each subject.Each black circle represents an individual human subject enrolled inthis study. Values are enclosed in the density ellipse calculated fromthe bivariate normal distribution fit to the X and Y variables at the95% confidence level. The criteria represented along the x-axis aredefined in Table 4. The values in the upper right corner are thePearson, Spearman and Huber M-value coefficients for this correlation.

FIG. 3 shows receiver operator characteristic (ROC) curves that quantifyand compare the ability of different tests to diagnose insulinresistance, as defined by the McAuley Index. top panel: plasma water T₂;middle panel: fasting glucose; bottom panel: HbA1c.

FIG. 4. Modified Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence formeasuring water T₂ in human serum or plasma using benchtop time-domainNMR. In contrast to NMR spectroscopy, the time points for theexponential decay curve are acquired directly during the CPMG pulsescheme, during the middle of the Δ delay between successive 180° pulses,as designated by the arrow. For the current study, the first 180° pulseand Δ delay were added prior to the CPMG scheme to achieve partial watersuppression and eliminate radiation damping. The Δ delay was tuned to0.95*T₁ for each sample, which corresponds to suppression of the waterto 23% of its full intensity. The τ delay was kept short (0.19 ms) toeliminate any possible impact of translational diffusion on T₂ in aninhomogeneous B_(o) field. For all experiments, RD was set to 5*T₁,which corresponds to ˜8 sec for serum or plasma; DE=5, NP=5600 andNS=16. The total experiment time was 6.4 minutes. Analysis of theresulting exponential decay curves is discussed in Materials andMethods. Abbreviations: RD, relaxation delay; DE, dummy echoes; NP,number of decay points acquired; NS, number of scans.

FIG. 5 illustrates an example system architecture in which animplementation of techniques for health screening using T₁ and/or T₂values for water may be carried out.

FIG. 6 shows a block diagram illustrating components of a computingdevice or system used in some implementations of an apparatus for healthscreening using T₁ and/or T₂ values for water.

FIG. 7 presents a schematic overview of the proposed linkage betweenmetabolic abnormalities, traditional clinical measures and plasma waterT₂.

BRIEF DESCRIPTION OF THE TABLES

Table 1: Characteristics of the Human Study Group, n=51.

Table 2: Biomarkers Measured in this Study.

Table 3: Bivariate correlation coefficients for plasma water T₂ withmarkers of insulin sensitivity and glucose tolerance (A), proteinconcentration and viscosity (B), inflammation (C), and cholesterolmetabolism (D).

Table 4: Single and Multiple Regression Models for Plasma Water T₂.

Table 5: Mean plasma water T₂ values for conditions and measuresassociated with early insulin resistance syndrome.

Table 6: Sensitivity, specificity and area-under-the-curve (AUC)parameters indicating the ability of various measures to diagnoseinsulin resistance (as defined by the McAuley Index) in normoglycemicsubjects, n=46.

DETAILED DISCLOSURE OF THE INVENTION

The term “about” is used in this patent application to describe somequantitative aspects of the invention, for example, time. It should beunderstood that absolute accuracy is not required with respect to thoseaspects for the invention to operate. When the term “about” is used todescribe a quantitative aspect of the invention, the relevant aspect maybe varied by up to ±10%. As used herein, the term “subject” refers to ahuman or non-human animal, such as a rat, mouse, pig, dog, cat, horse orany other animal, including animal models of human diseases.

For the purposes of this invention, T₂ refers to the NMR spin-spinrelaxation time constant. Surrogate measures that approximate T_(z),such as T₂* (the decay time constant from a free induction decay curve),or LW, the linewidth of a peak from a Fourier transformed NMR spectra,or any other representation of the NMR data that permits inferences orestimates of the T₂ relaxation rate can be used as alternativesurrogates for T₂. Likewise, T₁ refers to the spin-lattice relaxationtime constant. However, any surrogate measures that permit one toestimate or make inferences about T₁ can also be used as a surrogate forT₁ values. Where the terms “T₂ and/or T₁ data” or “T2 and/or T1 values”are used, it should be understood that surrogate measures can besubstituted for these terms.

The subject application discloses a method that involves at least threesteps: (1) acquisition of a NMR relaxation decay or recovery curve forplasma, serum or whole blood samples, or for tissues monitored fromoutside the body, (2) analysis of the relaxation decay or recovery curveto extract the T₂ and/or T₁ relaxation times (or T₂ or T₁ surrogates)for water, and (3) conversion of the water T₂ and/or T₁ values (orsurrogate values therefor) into a measure of someone's health status(referred to as a T₂ or T₁ health score depending on the valueassociated with the score). The T₁ and/or T₂ health score utilizes astatistical database derived from previous studies of subjects havingvarying degrees of metabolic abnormalities, such as inflammation,insulin resistance, lipid abnormalities (dyslipidemia), oxidativestress, brain abnormalities or other disorders, and provides a measureof a subject's overall metabolic and brain health status. Specifically,the disclosed method detects or rules out hidden problems such asinflammation, insulin resistance, lipid abnormalities (dyslipidemia),oxidative stress, brain abnormalities or other disorders. In otherwords, the disclosed method identifies metabolic abnormalities that aresubclinical (hidden) by conventional diagnostic criteria (i.e.,undiagnosed metabolic abnormalities or metabolic abnormalities having norecognizable signs or symptoms that would permit for the diagnosis of agiven metabolic abnormality). The disclosed invention has value as afront-line health screening test and provides a subject with a T₂ and/orT₁ Health Score that provides individuals with an overall assessment oftheir metabolic and brain health. The T₂ and/or T₁ Health Score providesevidence of hidden (undiagnosed) abnormalities that could lead todisease in the future. Non-limiting examples of these abnormalitiesinclude, but are not limited to, inflammation, insulin resistance,neurological abnormalities, oxidative stress and lipid abnormalities.Early detection and subsequent intervention can remedy or delay themanifestation of disease arising from the abnormalities disclosed herein(e.g., atherosclerosis, etc.) Thus, if an apparently healthy subject hasa moderately low T₂ and/or T₁ Health Score, the subject can choose anintervention, such as an exercise program, and check the score 4-8 weekslater to see if the health score has improved. Alternatively, thesubject can alter its diet, take low dose aspirin or add a nutritionalsupplement, such as an antioxidant or a fish oil and assess the impactof this change in diet on the T₂ and/or T₁ Health Score. Subjects withthe lowest scores would be advised to visit their physician for a morecomplete workup to rule out a disease diagnosis and/or subjects can betreated with an appropriate therapeutic intervention. With respect tothe overall assessment of metabolic and brain health, subjects can beseparated into at least three categories. In some embodiments, thesubjects can be separated as follows on the basis of the T₂ and/or T₁Health Score (based on plasma T₂ values): >800: lowest likelihood ofmetabolic abnormalities; 700-800 or between 720 to 800: mediumlikelihood of metabolic abnormalities; <700 or <720: highest likelihoodof metabolic abnormalities, including early insulin resistance syndrome.Thus, subjects with a T₂ and/or T₁ Health Score of 800 or less can betreated according to the methods disclosed herein, subjected toheightened monitoring for the development of metabolic abnormalities orreferred to a health provider for further evaluation for a hiddenmetabolic abnormality, such as inflammation, insulin resistance, lipidabnormalities (dyslipidemia), oxidative stress, brain abnormalities orother disorders.

As discussed above, the subject application has determined that lowerwater T₂ and/or T₁ values (or surrogate values T₂ and/or T₁) in serumand plasma are indicative of increasing degrees of metabolicdysfunction, even in an essentially healthy population with clinical labvalues that fall in the normal reference ranges. The unique value ofTD-NMR is that an individual's overall health status with respect toinsulin resistance, inflammation, dyslipidemia and possibly oxidativestress can be assessed simultaneously in one measurement without havingto survey a large panel of clinical lab tests or biomarkers. Given itssimplicity, water T₂ and/or T₁ (4) (or surrogate values therefor) canserve as a screening tool for the early identification of individualswith hidden risk for diseases that are linked with metabolicabnormalities. Non-limiting examples of such diseases include, but arenot limited to, diabetes, coronary artery disease, and Alzheimer'sdisease (5, 6). These disorders account for much of the morbidity andmortality in modern societies. There is a continuing need for effectivescreening tools that can be implemented practically, inexpensively andbroadly across the population will have a place in P4 medicine:personal, predictive, preventative and participatory (7). The inventiondisclosed herein provides a solution to this continuing need. Thissubject application describes methods for determining an individual'soverall health status with respect to insulin resistance, inflammation,dyslipidemia, oxidative stress and brain abnormalities can be assessedsimultaneously in one measurement without having to survey a large panelof clinical lab tests or biomarkers by measuring water T₂ and/or T₁values (or surrogate values therefor) in samples obtained from asubject. In various embodiments, the samples are subjected to nopre-treatment or other sample manipulation. The method leverages theinformation content of all plasma and serum proteins, including the mostabundant ones, in developing T₂ and/or T₁ Health Scores. In one aspect,the method includes the of placing a small volume of a sample comprisingwater into a NMR instrument tuned to measure a particular nucleus, suchas ¹H, ²H or ¹⁷O, by applying a series of radiofrequency pulses withintermittent delays in order to measure spin-spin (“T₂”) and/orspin-lattice (“T₁”) relaxation time constants from the time-domain decayor recovery of the signal. In other embodiments surrogate values for T₂and/or T₁ can be obtained and used (e.g., T₂* (the decay time constantfrom a free induction decay curve), LW, the linewidth (LW) of a peakfrom a Fourier transformed NMR spectra, or any other representation ofthe NMR data that permits inferences or estimates of the T₂ relaxationrate). The delay from pulse to data acquisition can range from about 1to about 50 milliseconds after the start of pulse scheme that acquiresthe relaxation decay curve; about 16 to about 20 milliseconds after thestart of the pulse scheme; or about 19 milliseconds after the start ofthe pulse scheme. In some embodiments, the signal is used in a raw form,without the use of chemical shifts and without converting data into thefrequency domain by Fourier transform or other means. The method canalso be performed by, at least, partially suppressing the water signalprior to the beginning of a sequence used to record relaxation timeconstants in the time domain, analyzing the exponentially decaying NMRsignal in the time domain using single- or multi-exponential analysis,and comparing differences in the relaxation time constants for waterwithin a single human subject, or between subjects, to assess normal andabnormal water T₂ and/or T₁ values (or surrogate values therefor) thatare reflective of increased disease risk or active disease. In anotheraspect, the method comprises application of the disclosed method to aplurality of samples obtained from a plurality of subjects anddeveloping a database of T₂ and/or T₁ values (or alternatively,surrogate values such as T₂* (the decay time constant from a freeinduction decay curve), or LW, the linewidth of a peak from a Fouriertransformed NMR spectra, or any other representation of the NMR datathat permits inferences or estimates of the T₂ relaxation rate) forwater in said samples. The database can be used to provide a range ofvalues for individuals having, or at risk of developing, a disorder suchas insulin resistance, inflammation, dyslipidemia, oxidative stress andbrain abnormalities (e.g., lower cognitive scores or mild cognitiveimpairment that often precedes Alzheimer's disease or Parkinson'sdisease).

In some embodiments, the database can provide further guidance in thedevelopment of T₂ and/or T₁ Health Score (based on T₂ and/or T₁ values).For example, plasma T₂ and/or T₁ Health Scores >800 are indicative ofthe lowest likelihood of metabolic abnormalities; T₂ and/or T₁ HealthScore values of between 700-800 or 720 to 800 indicate a mediumlikelihood of metabolic abnormalities; and T₂ and/or T₁ Health Scorevalues of <700 or <720 are indicative of the highest likelihood ofmetabolic abnormalities.

In another aspect, the invention is a diagnostic kit that includes apulse time domain or frequency domain NMR instrument, a sample selectedfrom the group consisting of serum and plasma, and a database of T₂and/or T₁ data for water that correlates with a disorder such as insulinresistance, inflammation, dyslipidemia, oxidative stress and brainabnormalities (e.g., low cognitive scores or mild cognitive impairment).

The general principles of time domain pulse NMR are generally wellunderstood and familiar to persons of ordinary skill in the art and neednot be discussed in detail. In brief, however, a sample is positioned inan external magnetic field provided by a permanent magnet. This alignsthe magnetic moments of the hydrogen atoms with (or against) thepermanent magnetic field. Then, a radio frequency pulse is applied in adirection that provides a secondary (temporary) magnetic fieldperpendicular to the permanent magnetic field. This moves the magneticmoments of the hydrogen atoms away from their equilibrium state. Thetime duration of the pulse determines how far the magnetic moments move.The combined movement of many spins (many hydrogen atoms) generates asmall but detectable oscillating magnetic field that in turn induces analternating voltage that is measured as the NMR signal by a detectioncoil.

At the end of the pulse, the protons in the sample give up excess energyto their surroundings and relax back to the equilibrium state withrespect to the permanent magnetic field. This relaxation takes a certainamount of time, so that the NMR signal remains detectable for a periodof time that can range from several milliseconds to several seconds.Furthermore, the relaxing component of the NMR signal will becharacteristic of individual mobility domains, which in turn, helpidentify the molecules involved in the motions and the rate of themotions. Samples can be scanned and the NMR signal acquired multipletimes, such as between 1 and 256 times or up to 10 to 50 times.

In one embodiment, the hydrogen spin-spin relaxation rate constants (ortime constants) are measured using a low-field bench-top time-domain NMRanalyzer, and the relaxation rate constant for water is resolved througha single- or multi-exponential deconvolution algorithm. The analysis canbe made directly on serum, plasma, whole blood or intact tissue. Becauseof the relative simplicity and low cost, this method has potentialapplication to clinical testing for the detection of a disorder such asinsulin resistance, inflammation, dyslipidemia, oxidative stress andbrain abnormalities (e.g., low cognitive scores or mild cognitiveimpairment). Alternatively, the measurements can be made in conventionallow or high-field spectrometers, magnetic resonance imagers (MRI) or aportable, wearable NMR device. In one embodiment, a tube containing asample is placed into the bore of the magnet of a bench-top TD-NMRanalyzer. Typically, the analyzer can be operated at 5, 10, 20, 40 or 60MHz resonance frequency for hydrogen.

A Car-Purcell-Miniboom-Gill (CPMG) pulse sequence can, in someembodiments, be used to measure the exponential T₂ time-decay curve forwater. This pulse sequence effectively eliminates chemical shifts andmagnetic field inhomogeneity, permitting the measurement of T₂ values.Of course, any pulse sequence capable of measuring T₂ or surrogatemeasures of T₂ such as T₂* (the decay rate of a free induction decaysignal), NMR peak linewidth (typically the half-height linewidth of aFourier transformed NMR signal), or any other representation of the NMRdata that permits inferences or approximation of the T₂ relaxation rateand, if necessary, partially suppressing the water signal can be used inthe disclosed method. Although T₂ measurements can be linked withchemical shifts and measured in the frequency domain, the TD-NMRembodiment of this method measures T₂ in the time domain withoutchemical shifts. The resulting T₂ decay curve for human serum istypically multi-exponential. However, even though the curve ismulti-exponential, a rough estimate of water T₂ may be obtained using asingle exponential analysis. Thus, the individual exponential terms canbe deconvoluted and resolved with the use of an inverse Laplaciantransform. The mathematical calculation can be implemented using Xpfit,a public domain program, among other open-source or commerciallyavailable solutions. While the use of an inverse Laplace transform isexemplified in this application for the exponential analysis algorithm,any other suitable exponential analysis algorithm can be used for theanalysis of the exponential data acquired by the practice of thedisclosed methods. With respect to the exponential analysis of theacquired data, at least one exponential term is analyzed. In variousembodiments, between one and 10 terms are analyzed. Other embodimentsprovide for the analysis of up to 6 terms or up to three exponentialterms.

Plasma and serum water T₂ values from TD-NMR have been correlated withover 70 blood tests (Tables 2-6). Strong correlations exist betweenplasma water T₂, plasma viscosity and total serum protein concentration,particularly serum globulins (Table 1). Inflammatory markers alsocorrelated with plasma water T₂. These include the inflammatory markers:C-reactive protein, white blood cell counts and neutrophil counts.Plasma water T₂ also correlated with the following markers of insulinresistance: insulin C-peptide, HOMA2-IR, triglycerides and HbA_(1c).

Serum water T₂ values reveal a set of correlations similar to those ofplasma. Serum water T₂ correlates with a number of LDL-relatedcholesterol markers. Serum water T₂ also shows significant correlationswith serum protein, globulin and albumin concentrations as well as serumviscosity. Additionally, serum water T₂ also correlates with white bloodcell counts, neutrophil counts and C-reactive protein (inflammatorymarkers). Thus, serum water T₂ values can be used to assess the risk orpresence of disorders such as inflammation or dyslipidemia (lipiddisorders in a subject).

The disclosed methods can also be coupled with treatments (under thesupervision of a physician or appropriate licensed health care provider)for the disorders discussed herein for subjects identified to be at riskfor the development of diabetes, coronary artery disease, Alzheimer'sdisease, etc. For example, subjects with evidence of inflammation can betreated with a variety of anti-inflammatory agents. Non-limitingexamples of such agents include: non-steroidal anti-inflammatory agentssuch as ibuprofen, naproxen, aspirin, celecoxib, sulindac, oxaprozin,salsalate, diflunisal, piroxicam, indomethacin, etodolac, meloxicam,nambumetone, ketorolac tromethamine, and diclofenac; corticosteroids,such as beclomethasone, beclometasone, budesonide, flunisolide,fluticasone, tramcinolone, methylprednisone, prenisolone or prednisone.For patients showing evidence of insulin resistance, the patients can betreated by altering diet, initiating a diabetic treatment, increasingexercise or otherwise modifying behavior so as to reduce the likelihoodof developing diabetes arising from insulin resistance. For subjectsshowing evidence of a dyslipidemia, the subject can be treated with lowdose aspirin and/or statins (such as atorvastatin, fluvastatin,lovastatin, pitavastatin, pravastatin, rosuvastatin or simcastatin), oranother suitable lipid-lowering therapy.

FIG. 5 illustrates an example system architecture in which animplementation of techniques for health screening using T₁ and/or T₂values for water may be carried out. In the example illustrated in FIG.5, a health screening service 200 may receive information from an NMR210, used to process a subject 205 sample. Health screening service 200may output results, such as a health score or treatment information tosubject 205.

A device appropriate for a health screening service 200 may beimplemented as software or hardware (or a combination thereof) on adevice which may be an instantiation of system 300. Such a device may beor include computing systems or devices such as a laptop, desktop,tablet, reader, mobile phone, wearable device, “Internet of things”device, and the like.

An NMR device 210 may be laboratory device (such as an NMR or MRIinstrument), bench-top device, or even a portable device. A portable NMRdevice 210 may be capable of being worn (e.g., wearable), connected toor adjacent to a subject's skin through a biosensor. In such cases theNMR device 210 may communicate with the health screening service over awireless communications network, such as Bluetooth®.

Health screening service 200 may interact with a data store 220, whichcan store biomarkers and their associated T₁ and/or T₂ reference valuesand/or ranges for different sample types. Data store 220 may also storeadditional information, for example, treatment information and data setsderived from samples gathered from other subjects. All or part of datastore 220 may be instantiated on the same system as health screeningservice, or may be instantiated on multiple systems, connected by anetwork.

Communications and interchanges of data between components in theenvironment may take place over a network (not shown). The network caninclude, but is not limited to, a cellular network (e.g., wirelessphone), a point-to-point dial up connection, a satellite network, theInternet, a local area network (LAN), a wide area network (WAN), a Wi-Finetwork, an ad hoc network, an intranet, an extranet, or a combinationthereof. The network may include one or more connected networks (e.g., amulti-network environment) including public networks, such as theInternet, and/or private networks such as a secure enterprise privatenetwork.

FIG. 6 shows a block diagram illustrating components of a computingdevice or system used in some implementations of an apparatus for healthscreening using T₁ and/or T₂ values for water. For example, anycomputing device operative to run a health screening service 200 orintermediate devices facilitating interaction between other devices inthe environment may each be implemented as described with respect tosystem 300, which can itself include one or more computing devices. Thesystem 300 can include one or more blade server devices, standaloneserver devices, personal computers, routers, hubs, switches, bridges,firewall devices, intrusion detection devices, mainframe computers,network-attached storage devices, and other types of computing devices.The hardware can be configured according to any suitable computerarchitectures such as a Symmetric Multi-Processing (SMP) architecture ora Non-Uniform Memory Access (NUMA) architecture.

The system 300 can include a processing system 301, which may include aprocessing device such as a central processing unit (CPU) ormicroprocessor and other circuitry that retrieves and executes software302 from storage system 303. Processing system 301 may be implementedwithin a single processing device but may also be distributed acrossmultiple processing devices or sub-systems that cooperate in executingprogram instructions.

Examples of processing system 301 include general purpose centralprocessing units, application specific processors, and logic devices, aswell as any other type of processing device, combinations, or variationsthereof. The one or more processing devices may include multiprocessorsor multi-core processors and may operate according to one or moresuitable instruction sets including, but not limited to, a ReducedInstruction Set Computing (RISC) instruction set, a Complex InstructionSet Computing (CISC) instruction set, or a combination thereof. Incertain embodiments, one or more digital signal processors (DSPs) may beincluded as part of the computer hardware of the system in place of orin addition to a general purpose CPU.

Storage system 303 may comprise any computer readable storage mediareadable by processing system 301 and capable of storing software 302including health screening service 200 and/or data store 220. Storagesystem 303 may include volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data.

Examples of storage media include random access memory (RAM), read onlymemory (ROM), magnetic disks, optical disks, CDs, DVDs, flash memory,solid state memory, phase change memory, or any other suitable storagemedia. Certain implementations may involve either or both virtual memoryand non-virtual memory. In no case do storage media consist of apropagated signal. In addition to storage media, in someimplementations, storage system 303 may also include communication mediaover which software 302 may be communicated internally or externally.

Storage system 303 may be implemented as a single storage device but mayalso be implemented across multiple storage devices or sub-systemsco-located or distributed relative to each other. Storage system 303 mayinclude additional elements, such as a controller, capable ofcommunicating with processing system 301.

Software 302 may be implemented in program instructions and among otherfunctions may, when executed by system 300 in general or processingsystem 301 in particular, direct system 300 or processing system 301 tooperate as described herein for enabling health screening with T₂ and/orT₁ values. Software 302 may provide program instructions 304 thatimplement a health screening service 200 or subcomponents thereof.Software 302 may implement on system 300 components, programs, agents,or layers that implement in machine-readable processing instructions themethods described herein as performed by health screening service 200(as instructions 304).

Software 302 may also include additional processes, programs, orcomponents, such as operating system software, database managementsoftware, or other application software. Software 302 may also includefirmware or some other form of machine-readable processing instructionsexecutable by processing system 301.

In general, software 302 may, when loaded into processing system 301 andexecuted, transform system 300 overall from a general-purpose computingsystem into a special-purpose computing system customized to facilitatehealth screening with T₂ and/or T₁ values. Indeed, encoding software 302on storage system 303 may transform the physical structure of storagesystem 303. The specific transformation of the physical structure maydepend on various factors in different implementations of thisdescription. Examples of such factors may include, but are not limitedto, the technology used to implement the storage media of storage system303 and whether the computer-storage media are characterized as primaryor secondary storage.

System 300 may represent any computing system on which software 302 maybe staged and from where software 302 may be distributed, transported,downloaded, or otherwise provided to yet another computing system fordeployment and execution, or yet additional distribution.

In embodiments where the system 300 includes multiple computing devices,one or more communications networks may be used to facilitatecommunication among the computing devices. For example, the one or morecommunications networks can include a local, wide area, or ad hocnetwork that facilitates communication among the computing devices. Oneor more direct communication links can be included between the computingdevices. In addition, in some cases, the computing devices can beinstalled at geographically distributed locations. In other cases, themultiple computing devices can be installed at a single geographiclocation, such as a server farm or an office.

A communication interface 305 may be included, providing communicationconnections and devices that allow for communication between system 300and other computing systems (not shown) over a communication network orcollection of networks (not shown) or the air. Examples of connectionsand devices that together allow for inter-system communication mayinclude network interface cards, antennas, power amplifiers, RFcircuitry, transceivers, and other communication circuitry. Theconnections and devices may communicate over communication media toexchange communications with other computing systems or networks ofsystems, such as metal, glass, air, or any other suitable communicationmedia. The aforementioned communication media, network, connections, anddevices are well known and need not be discussed at length here.

It should be noted that many elements of system 300 may be included in asystem-on-a-chip (SoC) device. These elements may include, but are notlimited to, the processing system 301, a communications interface 305,and even elements of the storage system 303 and software 302.

Alternatively, or in addition, the functionality, methods and processesdescribed herein can be implemented, at least in part, by one or morehardware modules (or logic components). For example, the hardwaremodules can include, but are not limited to, application-specificintegrated circuit (ASIC) chips, field programmable gate arrays (FPGAs),system-on-a-chip (SoC) systems, complex programmable logic devices(CPLDs) and other programmable logic devices now known or laterdeveloped. When the hardware modules are activated, the hardware modulesperform the functionality, methods and processes included within thehardware modules.

Materials and Methods Materials and Methods

Subject Recruitment.

Human subject volunteers were recruited with informed consent into twoprotocols approved by the Institutional Review Board of the Universityof North Texas Health Science Center in Fort Worth (UNTHSC). Oneprotocol recruited apparently healthy adult subjects from the studentand staff population of UNTHSC, including some spouses and significantothers. The second protocol recruited community members enrolled in theHealth and Aging Brain Study at UNTHSC (8). Exclusion criteria for thecurrent study included diabetes (HbA_(1C)>6.4), acute/chronic infectionor illness (C-reactive protein >10), or not fasting for at least 12hours. Characteristics of the human study group are detailed in Table 1.

Plasma and Serum Preparation.

Fasting blood samples were drawn in the morning by a trained nurse orphlebotomist. For plasma preparation, blood was drawn into lavender-toptubes containing EDTA as the anticoagulant. For serum, blood was drawninto plain glass red-top tubes lacking any gel separator or clotactivators (BD model 366441) in order to avoid potential interference ofadditives with TD-NMR or viscosity testing. Blood obtained for NMRLipoProfile analysis (LabCorp/LipoScience) was drawn into black-toptubes specialized for that purpose. Every effort was made to collectenough blood from each subject to perform all 70+ planned measurements.However, there were situations where the amount of blood collected froma given subject was not sufficient or samples were rejected by thetesting lab. That variability accounts for the test-to-test differencesin sample size (n) in the statistical analyses.

Blood Sample Analysis and Banking.

The plasma and serum samples were processed immediately after each blooddraw. The samples were centrifuged to remove pelleted blood cells,followed by a second low speed spin of the supernatant to removeresidual blood cells. The TD-NMR water T₂ measurements were performedfive times on a sample of fresh plasma followed immediately by fiverepeats on fresh serum such that all water T₂ measurements werecompleted within ˜2 hours after the blood draw. Likewise, viscosity wasmeasured in house on fresh serum and plasma samples within a few hoursof the blood draw using a VISCOLab3000 instrument as described elsewhere(9). Aliquots of fresh serum were sent on ice to Atherotech, Inc. forVertical Autoprofile (VAP) advanced lipoprotein testing, as well as todetermine LDL-P, hs-CRP, GGT, homocysteine, and Lp(a). Aliquots ofplasma and serum were frozen at −80° C. prior to in-house analysis usingassay kits: apolipoprotein E concentration (Abcam, Ab108813); ORACantioxidant capacity (Cell Biolabs, STA-345), protein carbonyl content(Cell Biolabs STA-307), and HNE (Cell Biolabs, STA-838); and free fattyacids (BioAssay Systems, EFFA-100). All other testing of serum andplasma samples was performed by LabCorp, Quest Diagnostics and theiraffiliates including LipoScience (NMR LipoProfile) and OmegaQuant(Omega-3 Index). Plasma aliquots for amino acid analysis were frozenimmediately after preparation and stored at −80° C. prior to shipment toQuest.

Samples for Controlled Experiments.

All samples were prepared with phosphate-buffered saline, pH 7.4.Reagents obtained from Sigma-Aldrich included human serum albumin(Catalog No. A8763), human γ-globulin (G4386), uric acid (U2625),DL-lactic acid (69785), malondialdehyde tetrabutylammonium salt (63287)and glyceraldehyde (G5001). Reagents obtained from Fisher Scientificincluded adenosine-tri-phosphate (S25123), D-glucose (D15-500), urea(BP169-500) and cupric sulfate (S25285).

Benchtop Time-Domain NMR Relaxometry.

Measurements of T₂ and T₁ were performed at 37° C. using a Bruker mq20Minispec benchtop time-domain NMR instrument equipped with a 10 mmvariable temperature probe (Model H20-10-25-AVGX). The 10 mm NMR tube,which included a Wilmad coaxial insert, was filled to a sample height of1 cm, corresponding to a sample volume of ˜70 microliters.

The pulse sequence for T₂ measurement is illustrated in FIG. 1 of ref(10). In our experience, a critical factor in obtaining high qualityTD-NMR data with aqueous samples is carefully tuning the delta delay toavoid radiation damping, particularly when 10 mm tubes are used withouta coaxial insert. Radiation damping occurs when the additional magneticfield created by the intense oscillating water signal distorts theperformance of CPMG pulse scheme (11). Radiation damping manifestsitself by a non-random oscillatory artifact observed in the residuals ofthe fit after inverse Laplace transform. We determined empirically thata delta delay of 0.95*T₁ (leading to a water signal that is ˜23% of itsfull intensity) provides a level of suppression of the water sufficientto avoid radiation damping, while still maximizing the overall signalintensity of the water and the other lipid/protein peaks for analysis.Even after partial suppression, the intensity of the water signal wasstill sufficiently intense to measure water T₂ with high precision afteronly 8-16 scans. In this regard, the goal of water suppression in TD-NMRis different from that of frequency-domain NMR spectroscopy, asessentially complete suppression of the water is typically desired inthe latter.

Radiation damping could also be eliminated by reducing the amount ofsample in the probe. Use of a Wilmad coaxial insert reduced the samplevolume down to ˜70 microliters (from ˜600 microliters). With the insert,it was no longer necessary to suppress the water signal.

Another unique aspect to this TD-NMR pulse scheme was the delayedacquisition of the data points, which began 19 ms after the beginning ofthe CPMG scheme. This strategy de-emphasizes the very fast processes atthe beginning of the decay curve in order to emphasize the slowerprocesses such as the water. This delayed acquisition scheme reduces thenumber of exponential terms, simplifying the inverse Laplace transformcalculation. If attempts are made to fit the data using too manyexponential terms, the calculation can become unstable, as it becomes amathematically ill-posed problem. Such overfitting is evidenced by poorrun-to-run precision, which was not observed using the current protocol.

For quantification of serum and plasma water T₂ values, each raw CPMGdecay curve was analyzed using an inverse Laplace transform asimplemented in the discrete components analysis of XPFit (see WorldwideWebsite: softscientific.com/science/xpfit.html). An importantconsideration for sample-to-sample comparisons is to restrain the numberof exponential terms to a consistent number; the data obtained with 16scans were fit to three terms. Less than three is not adequate to fitthe data, as evidenced by poor residuals. Given the high signal-to-noiseratio of the water, it is not difficult to obtain stable fittingsolutions for serum or plasma water T₂ data recorded with 16 scans. Forillustrative purposes, the T₂ profile distributions shown in FIG. 1 weregenerated using CONTIN (s-provencher.com), even though the T₂ valueswere quantified using XPFit as described above. The water T₂ values fromCONTIN and XPFit are essentially identical. XPFit has the advantage ofbeing able to constrain the number of exponentials and employs anon-negative truncated single value decomposition algorithm, whichstabilizes the calculation.

Statistical Analysis.

The correlation, linear regression and statistical analyses wereperformed using GraphPad Prism v. 6.05 (GraphPad Software, Inc.) and JMPPro v. 12.1.0 (SAS, Inc.). Some of the guiding principles for thestatistical analyses were derived from the book by Motulsky (12). Thenull hypothesis states that there is no correlation between thevariables being compared. The two-tailed p value defines the probabilityof observing a correlation as strong or stronger if the null hypothesiswere true. For example, for r=−0.6 and p<0.01, there is less than 1%probability of observing a correlation this strong or stronger by randomchance; thus, the null hypothesis is rejected. For each correlation thatmet p-value thresholds, we inspected the plot to ensure that thecorrelation was not heavily influenced by one or two outliers. Sampleplots are provided in FIG. 1. Regression residuals were analyzed inGraphPad Prism using the simple strategy outlined in the web link withinref. (13).

Example 1

Unlike conventional frequency-domain NMR spectroscopy, benchtoptime-domain NMR relaxometry is based on the exponential analysis of theraw time-domain signal to extract a distribution of relaxation timeconstants (10). This distribution is referred to as a T₂ profile, whichsuperficially resembles a NMR spectrum, but has a different x-axis and afundamentally different meaning. The T₂ profile is calculated using aninverse Laplace transform and represents the distribution of T₂ valuesconsistent with the exponentially-decaying time-domain signal. Anexample of a T₂ profile for human serum is provided in FIG. 2B of ref(10). The water peak represents approximately 94% of the totalintensity, and the remaining 6% is captured in 2-3 tiny peaks arisingfrom the non-labile hydrogen atoms from blood lipids and proteins. Anoverlay of the water peak from 29 human subjects, displayed in FIG. 2Cof ref (10), reveals the remarkably wide variation in serum water T₂values among apparently healthy volunteers.

The characteristics of the current human subject group are presented inTable 1. Overall, this is an apparently healthy group of adultvolunteers spanning a wide age range. The exclusion criteria werediabetes (HbA_(1c)≧6.5, fasting glucose ≧125 mg/dL or prior diagnosis)or acute/chronic illness (C-reactive protein >10 or history of recent orchronic illness). In all cases, the mean values for various diagnosticmarkers fell within the normal reference ranges, near the middle ofthose ranges. The range of values across the study group coincided wellwith the normal reference ranges, although specific values for a fewindividuals were outside of those ranges. With respect to glycemia, 46of the 51 subjects had fasting glucose levels <100 mg/dL and theremaining 5 did not exceed 115.

The plasma and serum water T₂ values from TD-NMR showed considerablevariation across the study population (Table 1). To identify the factorsgoverning the variation, we measured over 70 diagnostic tests and bloodbiomarkers and correlated them with plasma and serum water T₂ values(Table 2). The statistically-significant bivariate correlationcoefficients for plasma water T₂ are listed in Table 3, and examples ofthe corresponding scatterplots are shown in FIG. 1. The strongestcorrelations were observed between plasma water T₂ and fasting insulinor insulin C-peptide, as well as various indices derived from insulin,glucose and/or triglycerides (Table 3, (A)). In addition, a strongcorrelation was observed with total serum protein concentration, and toa lesser extent serum globulins and viscosity, but not serum albumin(Table 3, (B)). Moreover, strong correlations were observed with markersof inflammation (Table 3, (C)), especially C-reactive protein and whiteblood cell count. Finally, correlations were observed with a variety ofcholesterol-rich lipoprotein markers (Table 3, (D)). Plasma water T₂measurements did not correlate with body-mass index or age.

Serum water T₂ values revealed bivariate correlations similar to thoseof plasma, although the insulin-related variables had somewhat lowercorrelation coefficients.

The bivariate correlations led us to consider the factors that maycontribute directly to the variation in plasma and serum water T₂, aswell as those that may be indirectly linked through another variable ora network of variables. Human blood plasma and serum are complexmixtures containing hundreds of different proteins and lipoproteins aswell as numerous small molecule metabolites. At first thought,de-convoluting these myriad variables would seem to be hopelesslycomplex. However, the ten most abundant proteins in plasma (albumin,IgG, transferrin, fibrinogen, IgA, alpha2-macroglobulin, IgM,alpha₁-antitrypsin, C3 complement and haptoglobin) account for over 90%of total protein mass and the top two, nearly 80% (14). So identifyingthe primary contributors to water T₂ may be feasible.

We used three approaches to tease apart some of these factors. The firstapproach utilized a principal components analysis to identify clustersof variables in this dataset that are most closely related. The secondapproach utilized regression residuals (13), eliminating the influenceof one variable while examining the correlations of plasma water T₂ withthe remaining ones. The third approach made use of multiple regressionmodels to control for the effect of confounders and identify independentcontributors to water T₂.

Table 4 lists the parameters associated with the best multipleregression models identified for plasma water T₂. These models contained2-5 terms. The most prominent and independent contributors to plasmawater T₂ were insulin c-peptide, total serum protein and white bloodcell count, with smaller contributions from HbA_(1c) and totalcholesterol.

Table 5 uses a case-control format to compare the mean plasma T₂ valuesfor different measures of hyperinsulinemia, dyslipidemia, inflammationand acid-base abnormalities. In all cases, the differences in the meanswere statistically significant. The differences were greatest withcombinations of two or three metabolic conditions associated with earlyinsulin resistance syndrome. FIG. 2 shows the progressive decrease andstrong inverse correlation between plasma water T₂ values and the numberof criteria met. The criteria refer to the measures listed in Table 5.

Table 6 lists the parameters obtained from receiver operatorcharacteristic (ROC) curves, which quantify the relative ability ofdifferent biomarkers to diagnose insulin resistance, as defined by theMcAuley Index. Sample ROC curves are shown in FIG. 3. The best curvesstay closest to the left vertical and top axes, giving the largest valueof area under the curve. Plasma water T₂ displayed the highest AUC andthe best combination of sensitivity and specificity—superior to fastingglucose and hemoglobin A_(1c), the tools widely used for diabetesscreening and risk assessment.

These result of this study reveal, for the first time, the strongrelationship between plasma water T₂ and components of the early insulinresistance syndrome. They demonstrate that plasma water T₂ is asensitive and specific biomarker for insulin resistance—superior toglucose and hemoglobin A1c—and show the promise for plasma water T₂ tobecome a new diagnostic test for insulin resistance and for diabetesscreening and risk assessment. Finally, the current results indicate thepotential for using plasma water T₂ for routine health monitoring.

Lower values of water T₂ in serum and plasma are indicative ofincreasing degrees of metabolic dysfunction, even in apparently healthyhuman subjects. The unique value of this approach is that health statuswith respect to insulin resistance, low-grade inflammation, dyslipidemiaand acid-base abnormalities can be assessed simultaneously in onemeasurement without having to order a panel of clinical lab tests orbiomarkers. One could envision the development of a T₂ Health Score, apractical screening tool for the early identification of hiddenabnormalities in healthy subjects, or for monitoring the effects ofexercise or changes in diet.

Early detection and correction of subclinical abnormalities in healthyindividuals could prevent the progression to serious diseases likediabetes, coronary artery disease, and possibly Alzheimer's disease.These disorders account for much of the morbidity and mortality inmodern societies. Effective screening tools that can be implementedpractically, inexpensively and broadly across the population will have aplace in P4 medicine: personal, predictive, preventative andparticipatory (7).

Although this study focused on the analysis of blood plasma and serum,it is conceivable that similar information could be extracted from wholeblood, after correcting for hematocrit. Conversely, information could begleaned about blood cells after correcting for plasma protein levels.Given the intensity of the water NMR signal, it should be feasible tomonitor the mobility of water in blood from outside of the body—withoutdrawing blood—using a TD-NMR-enabled finger device, earlobe clip or awristwatch-like device linked to a smart phone. This concept is notfar-fetched, as compact, portable NMR devices are already in use in theindustry.

It should be understood that the examples and embodiments describedherein are for illustrative purposes only and that various modificationsor changes in light thereof will be suggested to persons skilled in theart and are to be included within the spirit and purview of thisapplication and the scope of the appended claims. In addition, anyelements or limitations of any invention or embodiment thereof disclosedherein can be combined with any and/or all other elements or limitations(individually or in any combination) or any other invention orembodiment thereof disclosed herein, and all such combinations arecontemplated with the scope of the invention without limitation thereto.

TABLE 1 Characteristics of the Human Study Group, n = 51 (insulinsensitivity and glucose tolerance (^(A)), protein concentration andviscosity (^(B)), inflammation (^(C)), and cholesterol metabolism(^(D))) Parameter Mean ± SD Range Reference Values¹ Age 40 ± 16 23-80n/a Gender n/a 24 female n/a 27 male Body-Mass 26.4 ± 5.2  18.2-45.1 <25normal weight Index (kg/m²) 25-30 overweight ≧30 obese Plasma T₂ (ms)758.6 ± 65.1  633-878 n.d. Serum T₂ (ms) 814.6 ± 58.0  692-927 n.d.Total serum protein (g/dL)^(B) 7.1 ± 0.4 6.2-7.9 6.1-8.1 Serum albumin(g/dL)^(B) 4.5 ± 0.3 3.6-5.0 3.6-5.1 Serum globulins (g/dL)^(B) 2.7 ±0.4 1.8-3.3 1.9-3.7 WBC count (×10³/μL)^(C) 6.5 ± 1.6  3.9-11.2 3.8-10.8 Neutrophil 3.6 ± 1.3 1.8-7.3 1.5-7.8 count (×10³/μL)^(C)hs-CRP (mg/L)^(C) 2.5 ± 2.3 0.1-9.4 <3.0 (low-to-average CV risk)3.1-10.0 (high CV risk) >10.0 (infection/inflammation) Glucose(mg/dL)^(A) 91.1 ± 7.6   78-115 <100 non-diabetic 100-125 (pre-diabetic)HbA_(1c) (%)^(A) 5.5 ± 0.3 4.9-6.2 <5.7 (non-diabetic) 5.7-6.4(pre-diabetic) Insulin C-peptide (ng/mL)^(A) 2.1 ± 0.9 0.9-5.1 0.8-3.9(>2.85, IR) Insulin (μU/mL)^(A) 9.3 ± 6.3  2.2-40.1 2.0-19.6 (>12.2, IR)Triglycerides (mg/dL)^(A) 112 ± 59   42-321 <150 Total cholesterol(mg/dL)^(D) 189 ± 39  111-291 <200 HDL-C (mg/dL)^(D) 56 ± 12 32-85 ≧40(male); ≧50 (female) LDL-C (mg/dL)^(D) 111 ± 35   42-191 <130 Sodium(mmoles/L) 140 ± 3  131-146 135-146 Total CO₂, serum 24 ± 3  16-29 19-30(mmoles/L) ¹Reference values from Quest Diagnostics and Atherotech

TABLE 2 Biomarkers Measured in this Study TD-NMR Markers: plasma waterT₂, T_(2sa), T_(2sp), T_(2c); serum water T₂, T_(2sa), T_(2sp), T_(2c)(insulin sensitivity and glucose tolerance (^(A)), protein concentrationand viscosity (^(B)), inflammation (^(C)), and cholesterol metabolism(^(D))) Category Statistical Correlation with T₂ ^(†) Did not correlatewith T₂ ^(†) Protein, viscosity, total serum protein, serum albumin,α1-antitrypsin, AST, ALT, liver function serum globulins (calc), serumGGT markers^(B) viscosity, plasma viscosity Inflammation, blood hs-CRP,WBC, neutrophils, RBC, hematocrit, cell and oxidative monocytes,eosinophils, basophils, hemoglobin, MCV, MCH, stress markers^(C)platelets, RDW, anion gap corrected MCHC, lymphocytes, for albuminconcentration, HNE, ORAC antioxidant TNFα*, sICAM*, I-309*, factor VII*capacity Cholesterol/lipid Total cholesterol, HDL-C, non-HDL- Lp(a),EPA, AA, apoAI, markers^(D) C, LDL-C, LDL-P, LDL size, smallphospholipids, apoE LDL-P, HDL-P, VLDL-C, remnant-C, apoB, DHA, omega-3index Insulin resistance & insulin, insulin C-peptide, HbA_(1c),glucose, free fatty acids, diabetes markers^(A) HOMA2-IR,-% B,-% S,triglycerides, body-mass index IR Score (LipoScience) Electrolytemarkers chloride, bicarbonate, anion gap sodium, potassium, calciumKidney function blood urea nitrogen (BUN)*, estimated creatinine markersglomerular filtration rate (eGFR)* Thyroid function thyroid stimulatinghormone (TSH) free T4 markers ^(†)In this table, a correlation isdefined as one where p < 0.05 for at least two of three of thefollowing: Pearson, Spearman, or M-value correlation coefficients. Theindividual coefficients and statistics are provided in Table 3.

TABLE 3 Bivariate correlation coefficients for plasma water T₂ withmarkers of insulin sensitivity and glucose tolerance (^(A)), proteinconcentration and viscosity (^(B)), inflammation (^(C)), and cholesterolmetabolism (^(D)). Biomarker¹ N r (Pearson) ρ (Spearman) M-estimator(Huber) Insulin C-peptide^(2,A) 50 −0.67**** −0.65**** −0.70****Insulin^(2,A) 50 −0.63**** −0.59**** −0.64**** McAuley Index^(A) 50+0.64**** +0.66**** +0.66**** HOMA-IR^(2,3,A) 50 −0.67**** −0.66****−0.71**** QUICKI^(A) 50 +0.64**** +0.60**** +0.68**** FIRI^(A) 50−0.64**** −0.59**** −0.67**** Glucose/Insulin Ratio^(2,A) 50 +0.61****+0.58**** +0.58**** Glucose^(2,A) 50 −0.37** −0.40** −0.43** HbA_(1C)^(A) 49 −0.52*** −0.55*** −0.59**** TG^(2,A) 50 −0.49*** −0.51***−0.52*** TG/HDL Ratio^(A) 50 −0.44** −0.43** −0.52*** Total Protein,Serum^(B) 49 −0.57**** −0.57**** −0.60**** Serum Globulins^(B) 49−0.44** −0.44** −0.45** Serum Viscosity^(2,B) 46 −0.41** −0.44**−0.52*** C-reactive Protein^(C) 49 −0.50*** −0.49*** −0.52*** WBCCount^(2,C) 49 −0.51*** −0.51*** −0.54**** Neutrophil Count^(2,C) 49−0.46*** −0.42** −0.40** Eosinophil Count^(2,C) 48 −0.35* −0.35* −0.34*Platelet Count^(2,C) 49 −0.34* −0.34* −0.43** LDL-C^(2,D) 49 −0.42**−0.44** −0.39** non-HDL-C^(2,D) 49 −0.46*** −0.47*** −0.43**VLDL-C^(2,D) 49 −0.41** −0.42** −0.48*** IDL-C^(2,D) 49 −0.31* −0.34*−0.33* Remnant-C^(2,D) 49 −0.35* −0.40** −0.40** LDL-C/HDL-C Ratio^(2,D)49 −0.43** −0.46*** −0.54**** Total C^(2,D) 49 −0.40** −0.43** −0.39**ApoB^(2,D) 49 −0.47*** −0.50*** −0.48*** ApoB/ApoAI^(2,D) 49 −0.44**−0.51*** −0.58**** LDL-p^(D) 48 −0.47*** −0.49*** −0.46*** *p < 0.05;**p < 0.01; ***p < 0.001; ****p < 0.0001 ¹All biomarkers were measuredfollowing a 12-hour overnight fast. Biomarkers were included in thetable if they demonstrated at least two of three correlationcoefficients with a p value < 0.05. ²Variable was natural-logtransformed in order to meet the condition of a normal (Gaussian)distribution. ³Calculated using fasting glucose and fasting insulinc-peptide as input.

TABLE 4 Single and Multiple Regression Models for Plasma Water T₂ ModelAdjusted R² Predictor Variables Coefficients p values 1 0.42 ln insulinc-peptide  −92.3 ± 15.3 <0.0001**** y-intercept   817.5 ± 12.3<0.0001**** 2 0.57 ln insulin c-peptide  −72.9 ± 14.0 <0.0001**** totalserum protein  −73.8 ± 16.0 <0.0001**** y-intercept    1333 ± 113<0.0001**** 3 0.64 ln insulin c-peptide  −1.7 ± 12.9 <0.0001**** totalserum protein  −64.1 ± 15.3 <0.0001**** ln WBC count  −76.3 ± 24.5  0.0033** y-intercept    1404 ± 107 <0.0001**** 4 0.67 ln insulinc-peptide  −58.6 ± 13.4 <0.0001**** total serum protein  −62.3 ± 14.6<0.0001**** ln neutrophil count  −47.4 ± 16.1   0.0001*** HbA_(1c) −45.8 ± 18.6   0.0182* y-intercept    1882 ± 171 <0.0001**** 5 0.69 lninsulin c-peptide  −48.8 ± 13.7   0.0010** total serum protein  −60.9 ±14.1 <0.0001**** ln neutrophil count  −45.9 ± 15.6   0.0052** HbA_(1c) −46.7 ± 18.0   0.0130* ln total cholesterol  −50.6 ± 24.6   0.0459*y-intercept    2124 ± 202 <0.0001****

TABLE 5 Mean plasma water T₂ values for conditions and measuresassociated with early insulin resistance syndrome Mean Plasma T₂ Values(ms) Conditions and Measures Cutoff Value No Yes Diff p value¹ I.Hyperinsulinemia any of 2 below 773.7 707.6 66.1   0.0008*** highfasting insulin ≧12.2 μIU/mL² 749.9 675.3 74.5   0.0063** high insulinC-peptide ≧2.85 mg/mL³ 746.3 666.2 80.1   0.0203* II. Dyslipidemia anyof 3 below 785.2 720.5 64.7   0.0008*** high fasting TG ≧132 mg/dL²772.9 714.4 58.5   0.0026** small, dense LDL pattern B or AB 770.5 716.554.0   0.0071** high LDL-p ≧1468 nmoles/L⁴ 767.9 719.9 48.0   0.0210*III. Inflammation any of 3 below 791.1 733.6 58.1   0.0011** highC-reactive ≧3.0 mg/L⁵ 774.1 723.4 50.7   0.0072** protein    highneutrophil count ≧4200 cells/μL4 770.3 717.0 53.3   0.0080** high serumglobulins ≧2.9 g/dL⁴ 774.3 727.4 46.9   0.0101* IV. Acid-base any of 2below 778.2 721.9 56.3   0.0010** abnormalities    low serum CO₂ ≦22meq/L 769.2 719.3 49.9   0.0077** high anion gap ≧19.8 meq/L 768.4 724.244.2   0.0212* V. 2 or more conditions see above 798.9 721.3 77.6<0.0001**** VI. 3 or more conditions see above 778.8 699.5 79.3<0.0001**** VII. Hyperinsulinemia plus see above 775.0 698.0 77.0<0.0001**** 1 or 2 more conditions ¹Unpaired t-test for data sets withconfirmed equal variances. ²As defined by McAuley et at. (ref).³Determined by linear regression of fasting insulin C-peptide vs.fasting insulin, interpolating the C-peptide value corresponding to aninsulin of 12.2 μIU/mL. ⁴Defined as upper quartile of subjects in thisstudy. ⁵Defined as lower quartile of subjects in this study.

TABLE 6 Sensitivity, specificity and area-under-the-curve (AUC)parameters indicating the ability of various measures to diagnoseinsulin resistance (as defined by the McAuley Index) in normoglycemicsubjects, n = 46.¹ Measure AUC Sensitivity Specificity Cutoff Value forIR Plasma water T₂ 0.90  86%  86% ≦718.8 ms Glucose 0.73  86%  59% ≧90.9mg/dL Glucose 0.73  14% 100% ≧100 mg/dL² HbA_(1c) 0.76 100%  50% ≧5.5%HbA_(1c) 0.76  57%  71%  ≧5.7%² LDL-p 0.87 100%  69% ≧1307 nmol/L hs-CRP0.67  83%  59% ≧1.6 mg/L Neutrophil count 0.62  86%  53% ≧3498 cells/μLSerum globulins 0.70 100%  42% ≧2.6 g/dL Serum total CO₂ 0.82  86%  70%≦23 mmol/L Anion gap, corrected 0.62  57%  86% ≧21 mmol/L ¹Parameterswere derived from the receiver operator characteristic curves shown inFIG. 3. Normoglycemic is defined as fasting glucose <100 mg/dL.²American Diabetes Association criteria for prediabetes

Abbreviations

-   AA: arachidonic acid-   ALT: alanine aminotransferase-   AST: aspartate aminotransferase-   BMI: body-mass index-   BUN: blood urea nitrogen-   CPMG: Carr-Purcell-Meiboom-Gill NMR pulse sequence to measure T₂-   DHA: Docosahexaenoic Acid-   EDTA: ethylene-diamine-tetra-acetic-acid-   eGFR: estimated glomerular filtration rate-   EPA: eicosapentaenoic Acid-   GGT: gamma glutamyl transpeptidase-   HABS: Health & Aging Brain Study at the UNT Health Science Center,    Fort Worth-   HABLE: Health and Aging Brains in Latino Elders, a sub-study of HABS-   HbA_(1C): glycated hemoglobin-   HDL-C: high-density lipoprotein cholesterol concentration-   HDL-P: high-density lipoprotein particle number concentration-   HNE: 4-hydroxynonenal-   HOMA2-% B: homeostatic model assessment version 2, % beta cell    function-   HOMA2-% S: homeostatic model assessment version 2, % insulin    sensitivity-   HOMA2-IR: homeostatic model assessment version 2, insulin resistance    index (see https://www.dtu.ox.ac.uk/homacalculator for HOMA2    definitions)-   hs-CRP: high-sensitivity C-reactive protein-   I-309: member of the CC subfamily of chemokines-   IR Score: insulin resistance score (from NMR LipoProfile,    LipoScience)-   LDL-C: low density lipoprotein cholesterol concentration-   LDL-P: low density lipoprotein particle number concentration-   Lp(a): lipoprotein (a) cholesterol concentration-   MCH: mean corpuscular hemoglobin-   MCHC: mean corpuscular hemoglobin concentration-   MCV: mean corpuscular volume-   MDA: malondialdehyde-   NMR, nuclear magnetic resonance-   T_(2a): regression residuals from a linear fit of plasma or serum    water T₂ vs. serum albumin-   T_(2c): regression residuals from a linear fit of plasma or serum    water T₂ vs. serum cholesterol-   T_(2g): regression residuals from a linear fit of plasma or serum    water T₂ vs. serum globulins (globulins=total serum protein−serum    albumin)-   T_(2p): regression residuals from a linear fit of plasma or serum    water T₂ vs. total serum protein-   T_(2v): regression residuals from a linear fit of plasma or serum    water T₂ vs. viscosity-   r: Pearson correlation coefficient-   r_(S): Spearman correlation coefficient, non-parametric-   R²: square of the Pearson correlation coefficient-   RDW: red cell distribution width-   Remnant-C: remnant lipoprotein particle cholesterol concentration-   sICAM: soluble intercellular adhesion molecule-   TD-NMR: time-domain nuclear magnetic resonance-   TG: serum triglyceride concentration-   TNFα: tumor necrosis factor alpha-   TSH: thyroid stimulating hormone-   [UA]: unmeasured anion concentration, in meq/L-   [UC]: unmeasured cation concentration, in meq/L-   VAP: Vertical AutoProfile test, Atherotech-   VLDL-C: very low density lipoprotein cholesterol concentration-   WBC: white blood cells

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1-49. (canceled)
 50. A method for determining the T₂ and/or T₁ health score of a subject comprising obtaining a NMR data set or spin relaxation curve for a sample with an NMR or MRI instrument tuned to measure a particular nucleus selected from ¹H, ²H, ³H or ¹⁷O, analyzing the data or curve to extract T₂ and/or T₁ relaxation times for water or surrogates thereof and converting the water T₂ and/or T₁ values into a measure of the health status of the subject (the T₂ and/or T₁ health score).
 51. The method according to claim 50, wherein the sample is a plasma sample, a serum sample, whole blood sample, a tissue sample or a subject, such as a human being.
 52. The method according to claim 50, wherein the method comprises a step of partially suppressing the water signal, for example with a 180-degree inversion pulse followed by a delay or any other suitable method for partial suppression of the water signal prior to recording the relaxation curve.
 53. The method according to claim 52, wherein the delay time is tuned to eliminate radiation damping while maximizing water signal intensity.
 54. The method according to claim 50, wherein the step of analyzing the exponentially decaying or recovering NMR signal comprises acquiring the relaxation curve and subjecting the data to exponential analysis, for example transforming the data with an inverse Laplace transformation, or using any other suitable exponential analysis algorithm.
 55. The method according to claim 54, wherein said exponential analysis comprises the analysis of one or more exponential terms, for example one to six exponential terms, two to four exponential terms or three exponential terms.
 56. The method according to claim 52, wherein data acquisition begins about 1 to about 50 milliseconds after the start of pulse scheme that acquires the relaxation decay curve; about 16 to about 20 milliseconds after the start of the pulse scheme; or about 19 milliseconds after the start of the pulse scheme
 57. The method according to claim 50, wherein said sample is scanned multiple times.
 58. The method according to claim 50, wherein the step of converting the T₂ and/or T₁ values into a measure of the health status of the subject comprises comparing the T₂ and/or T₁ values obtained for the sample from the subject with T₂ and/or T₁ values obtained from a database, said database comprising T₂ and/or T₁ values obtained for a range of subjects, from those who are normal and healthy to those having varying degrees of inflammation, insulin resistance, lipid abnormalities, oxidative stress, brain or cognitive abnormalities and/or other disorders.
 59. The method according to claim 50, said method comprising referring an individual subject with a plasma T₂ value less than about 800 to a registered dietician, certified personal trainer, physician or other licensed health provider to improve health and well-being, and prevent the onset of diabetes, coronary artery disease, myocardial infarction, ischemic vascular disease, stroke, cognitive impairment, neurodegenerative diseases and dementia, including Alzheimer's disease, or any other diseases that arise from metabolic abnormalities such as chronic, low-grade inflammation, insulin resistance, lipid/lipoprotein abnormalities and/or oxidative stress and/or treating the subject with an appropriate treatment for inflammation, insulin resistance, lipid abnormalities, oxidative stress, brain or cognitive abnormalities and/or other disorders.
 60. An apparatus for health-screening a subject comprising: a NMR or MRI instrument or portable device tuned to measure a particular nucleus selected from ¹H, ²H, ³H or ¹⁷O; one or more computer readable storage media; a processing system; a data store contained on the one or more computer readable storage media comprising: one or more reference T₂ relaxation times for water and a reference T₁ relaxation times for water, for one or more of a sample type, wherein the reference T₂ and/or reference T₁ values are associated with a health status; program instructions for a health screening service stored on the one or more computer readable storage media that direct the processing system to: acquire an NMR data set, relaxation decay or recovery signal, using the NMR instrument, from a sample of a particular sample type from the subject; analyze the NMR data set or relaxation curve to extract a T₂ relaxation time for water and/or a T₁ relaxation time for water, or a surrogate of T₂ or T₁, such as T₂* or water peak linewidth; identify the reference values for the particular sample type associated with the extracted T₂ and/or T₁ values or surrogate values and determine the health status associated with the reference values; and display a health score determined from the health status.
 61. The apparatus according to claim 60, wherein the sample type is a whole blood sample, a serum sample, a plasma sample, a tissue sample or a subject, such as a human being.
 62. The apparatus according to claim 60, wherein the NMR instrument is a wearable device or is a benchtop or portable time domain NMR instrument or is a NMR spectrometer or magnetic resonance imager.
 63. An apparatus comprising: one or more computer readable storage media; a processing system; program instructions stored on the one or more computer readable storage media that direct the processing system to: obtain an NMR data set or relaxation decay or recovery curve from a sample obtained from a subject, wherein the data set or relaxation curve was obtained using a NMR instrument tuned to measure a particular nucleus selected from ¹H, ²H, ³H or ¹⁷O; analyze the NMR data set or relaxation decay curve to extract a T₂ relaxation time for water and/or a T₁ relaxation time for water or a surrogate of water T₂ or T₁, such as T₂* or water peak linewidth; determine a health status value of the subject, wherein the health status is determined from water T₁ and/or T₂ values, or surrogates therefor, from the sample; and store the water T₂ and/or T₁ values, or surrogates therefor, in association with the health status in a data store contained on the one or more computer readable storage media.
 64. A method of treating a subject with a plasma water T₂ value less than about 800 and at risk for the development of hidden metabolic abnormalities selected from inflammation, insulin resistance, lipid abnormalities (dyslipidemia), oxidative stress, brain abnormalities or other disorders comprising obtaining a health score for a subject according to claim 50 and treating a subject having a health score lower than about 800 for said metabolic abnormality and/or referring said subject to a physician for further evaluation.
 65. The method according to claim 64, wherein said hidden metabolic abnormality is low grade inflammation and said subject is further evaluated for treatment with one or more anti-inflammatory agents and/or treated with one or more anti-inflammatory agents.
 66. The method according to claim 65, wherein said anti-inflammatory agent is selected from ibuprofen, naproxen, aspirin, celecoxib, sulindac, oxaprozin, salsalate, diflunisal, piroxicam, indomethacin, etodolac, meloxicam, nambumetone, ketorolac tromethamine, or corticosteroids selected from as beclomethasone, beclometasone, budesonide, flunisolide, fluticasone, tramcinolone, methylprednisone, prenisolone or prednisone.
 67. The method according to claim 64, wherein said metabolic abnormality is insulin resistance and said subject is further evaluated and/or treated to improve insulin sensitivity, said treatment to improve insulin sensitivity being selected from the start or modification of an exercise and physical activity program, alteration of diet, or other modification of behavior so as to improve insulin sensitivity and reduce the likelihood of developing diabetes arising from insulin resistance.
 68. The method according to claim 64, wherein the metabolic abnormality is dyslipidemia and said subject is referred to a physician for further diagnostic evaluation and/or treated with low dose aspirin, statins and/or other lipid-lowering agents.
 69. The method according to claim 68, wherein the statin is selected from atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin or simcastatin.
 70. The method according to claim 64, wherein the metabolic abnormality is oxidative stress and a subject is referred to a physician and/or registered dietician to receive nutritional advice and/or is evaluated and treated with anti-oxidants such as ascorbic acid (Vitamin C), vitamin E or other nutritional supplements, and/or evaluated and treated for any accompanying inflammation or insulin resistance.
 71. A method of referring an individual with a plasma water T₂ value less than about 800 to a registered dietician, certified personal trainer, physician or other licensed health provider, with the goal to improve health and well-being, and prevent the onset of diabetes, coronary artery disease, myocardial infarction, ischemic vascular disease, stroke, cognitive impairment, neurodegenerative disease and dementia, including Alzheimer's disease, or any other diseases that arise from hidden metabolic abnormalities such as chronic, low-grade inflammation, insulin resistance, lipid/lipoprotein abnormalities, oxidative stress and/or brain abnormalities. 